The invention relates generally to the field of signal conditioning. More particularly, the invention relates to an interoperating collection of adaptive filters supporting sensor signal prediction and that prediction""s application. The adaptive filter based sensor signal predictor(s) may be used to increase a sensor""s rate of response.
Many industrial control applications require a plurality of sensors. In some applications, the sensors have a response time that is inadequate to provide effective control of the system.
Sensor response time may be described as the period between changing physical conditions and a change in the sensor (electrical) output. Sensor sensitivity may be described as the amount of change necessary in the physical condition that is needed to initiate a similar change in the sensor output signal. Together, the response time and the sensitivity of the sensor control the output signal.
If a given sensor""s response time is N (i.e., N=50 milliseconds) and its sensitivity is S (i.e., S=+/xe2x88x9210 parts per million, for a chemical sensor) then N milliseconds after an S part per million change is experienced, the sensor will produce an approximately proportional, measurable change in the generated electrical signal. A control system external to the sensor, relying on the sensor, receives no benefit in polling or sampling the sensor any faster than it can physically respond, because no change (or an inaccurate change) in the electrical signal will be detected. If, for example, the external control system requires a feedback signal at higher sampling frequencies than the frequency response of the sensor, the conventional or classic solution is to develop a new sensor with different (improved) physical response characteristics. Development of new sensors however, is costly and potentially not possible.
Therefore, there is a need for a method and apparatus for improving sensor response times by predicting sensor outputs. Further, there is a need for inexpensive electronic devices that serve as interfaces between a closed (black box) control system and a given (chemical, pressure, temperature, etc.) sensor. Further still, there is a need for sensor predicting devices that utilize adaptive filter or neural network prediction algorithms to monitor sensor short-range past response and are able to predict the short-range future response of the sensor. Further still, there is a need for sensor predicting devices utilizing adaptive filter or neural network prediction algorithms that employ adaptive error correction and optionally provide interpolated output in the short time period that is required by the external control system.
An exemplary embodiment of the invention relates to a system for improving the response time of a sensor. The sensor includes an input configured to sense an environmental characteristic and an output configured to provide an electrical signal representative of the input. The system also includes a sampling system configured to sample the sensor output at discrete time intervals and provide sampled sensor output signals. Further, the system includes an adaptive filter including a plurality of inputs and at least one output, the plurality of inputs configured to receive sampled sensor output signals, and at least one output of the adaptive filter configured to provide at least one estimated future sensor output based on the plurality of sampled sensor output signals provided to the plurality of inputs to the adaptive filter.
An exemplary embodiment of the invention also relates to a method of predicting the output of a sensor. The sensor has an input for sensing an environmental characteristic, and the sensor having a sampled output representative of the input at a discrete time interval. The method includes providing, to a plurality of inputs of an artificial neural network, a plurality of discrete sensor outputs. Each discrete sensor output is representative of a sensor input at a different discrete time interval. The method also includes generating at least one output of the artificial neural network. The at least one output being an estimate of at least one future sensor output.
Further, an exemplary embodiment of the invention relates to a method of predicting sensor output. The method includes adapting weights of an artificial neural network. The method also includes receiving, by the artificial neural network, a plurality of discrete sensor outputs from p discrete time intervals, up to the time n. Further, the method includes generating an output of the artificial neural network, based on the sensor outputs from the p discrete time intervals. The output is representative of the predicted sensor output at time n+1.
Further still, an exemplary embodiment of the invention relates to a method of accelerating the output of a sensor. The method includes receiving by a first neural network a plurality of sensor outputs at discrete evenly spaced time intervals less than and including time n. The method also includes receiving by a second neural network a plurality of sensor outputs at discrete evenly spaced time intervals less than and including time n+m, where 0 less than m less than 1. The method further includes generating, by the first neural network, a predicted sensor output that is an estimate of the sensor output at time n+1. Further, the method includes generating, by the second neural network, a predicted sensor output that is an estimate of the sensor output at time n+1+m.